Method and apparatus for generating chemical structure using neural network

ABSTRACT

Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from Korean Patent Application No.10-2018-0098373, filed on Aug. 23, 2018, in the Korean IntellectualProperty Office, the disclosure of which is herein incorporated byreference in its entirety.

BACKGROUND 1. Field

The disclosure relates to methods and apparatuses for generating achemical structure using a neural network.

2. Description of the Related Art

A neural network refers to a computational architecture that models abiological brain. With the advanced neural network technologies, varioustypes of electronic systems have analyzed input data and generatedoptimized information by using neural networks.

In recent years, extensive research has been conducted into methods ofselecting chemical structures to be used in material development byevaluating properties of the chemical structures using neural networktechnologies. Particularly, there is a need to develop methods ofgenerating new chemical structures satisfying a variety of requirementsby using neural network technologies.

SUMMARY

Embodiments of the disclosure relate to methods and apparatuses forgenerating a chemical structure using a neural network. Also, providedare computer-readable recording media including a program, which, whenexecuted by a computer, performs the methods. The technical problems tobe solved are not limited to these as described, but there may be othertechnical problems.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

According to an aspect of an embodiment, there is provided a method ofgenerating a chemical structure by using a neural network apparatusincluding: inputting a descriptor of a chemical structure to a trainedneural network that generates a property value of a property of thechemical structure, the descriptor of the chemical structurerepresenting structural characteristics of the chemical structure andthe property of the chemical structure being a characteristic possessedby the chemical structure; determining an expression region forexpressing the property in the descriptor, the expression regioncomprising a bit position in the descriptor; and generating a newchemical structure by modifying a partial structure in the chemicalstructure, the partial structure corresponding to the expression region.

The determining of the expression region may include determining theexpression region for expressing the property in the descriptor by thetrained neural network performing an interpretation process to determinewhether the property value is expressed by the partial structure in thechemical structure.

The determining of the expression region may include determining theexpression region for expressing the property in the descriptor byapplying a layer-wise relevance propagation (LRP) technique to thetrained neural network, wherein an activation function applied to a nodeof the trained neural network may be designated as a linear function toapply the LRP technique to the trained neural network, and a mean squareerror (MSE) may be designated for optimization.

The generating of the new chemical structure may include: obtaining abit value of the bit position of the expression region in thedescriptor; and generating the new chemical structure by applying agenetics algorithm to the bit value of the bit position and modifyingthe partial structure corresponding to the expression region.

The generating of the new chemical structure may include: generating anew first chemical structure by modifying the partial structure in thechemical structure, the partial structure corresponding to theexpression region; inputting a descriptor for the new first chemicalstructure to the trained neural network to output a property value of aparticular property for the new first chemical structure; and generatinga new second chemical structure by changing a partial structure in thenew first chemical structure, the partial structure corresponding to theexpression region, when the property value of the particular propertyfor the new first chemical structure is less than a preset value, andstoring the new first chemical structure when the property value of theparticular property for the new first chemical structure is equal to orgreater than the preset value.

According to an aspect of an embodiment, there is provided a neuralnetwork apparatus configured to generate a chemical structure including:a memory configured to store at least one program; and a processorconfigured to drive a neural network by executing the at least oneprogram, wherein the processor is configured to: input descriptor of achemical structure to a trained neural network that generates a propertyvalue of a property of the chemical structure, the descriptor of thechemical structure representing structural characteristics of thechemical structure and the property of the chemical structure being acharacteristic possessed by the chemical structure; determine anexpression region for expressing the property in the descriptor, theexpression region comprising a bit position in the descriptor; andgenerate a new chemical structure by modifying a partial structure inthe chemical structure, the partial structure corresponding to theexpression region.

According to an aspect of an embodiment, there is provided a method ofgenerating a chemical structure by using a neural network apparatusincluding: inputting an image of a chemical structure to a trainedneural network that generates a property value of a property of thechemical structure, the image of the chemical structure representingstructural characteristics of the chemical structure and the property ofthe chemical structure being a characteristic possessed by the chemicalstructure; determining an expression region for expressing the propertyin the image, the expression region comprising one or more pixels in theimage; and generating a new chemical structure by modifying a partialstructure in the chemical structure, the partial structure correspondingto the expression region.

The determining of the expression region may include determining theexpression region for expressing the property in the image by thetrained neural network performing an interpretation process to determinewhether the property value is expressed by the partial structure in thechemical structure.

The determining of the expression region may include determining theexpression region for expressing the property in the image by applying alayer-wise relevance propagation (LRP) technique to the trained neuralnetwork, wherein an activation function applied to a node of the trainedneural network may be designated as a linear function to apply the LRPtechnique to the trained neural network, and a mean square error (MSE)may be designated for optimization.

The generating of the new chemical structure may include: obtainingpixel values of the one or more pixels of the expression region in theimage; and generating the new chemical structure by applying Gaussiannoise to the pixel values of the one or more pixels and modifying thepartial structure corresponding to the expression region.

The generating of the new chemical structure may include: when aplurality of expression regions expressing the particular property inthe image are present, obtaining coordinate information in the imagecorresponding to the plurality of expression regions; calculating acenter point in the image of the plurality of expression regions basedon the coordinate information and obtaining a pixel value of the centerpoint; and generating the new chemical structure by applying Gaussiannoise to the pixel value and modifying the partial structurecorresponding to the center point.

The generating of the new chemical structure may include: generating anew first chemical structure by modifying the partial structure in thechemical structure, the partial structure corresponding to theexpression region; inputting an image for the new first chemicalstructure to the trained neural network to output a property value of aparticular property for the new first chemical structure; and generatinga new second chemical structure by changing a partial structure in thenew first chemical structure, the partial structure corresponding to theexpression region, when the property value of the particular propertyfor the new first chemical structure is less than a preset value, andstoring the new first chemical structure when the property value of theparticular property for the new first chemical structure is equal to orgreater than the preset value.

According to an aspect of an embodiment, there is provided a neuralnetwork apparatus configured to generate a chemical structure including:a memory configured to store at least one program; and a processorconfigured to drive a neural network by executing the at least oneprogram, wherein the processor is configured to: input an image of achemical structure to a trained neural network that generates a propertyvalue of a property of the reference chemical structure, the image ofthe chemical structure representing structural characteristics of thechemical structure and the property of the chemical structure being acharacteristic possessed by the chemical structure; determine anexpression region for expressing the property in the image, theexpression region comprising one or more pixels in the image; andgenerate a new chemical structure by modifying a partial structure inthe chemical structure, the partial structure corresponding to theexpression region.

According to an aspect of an embodiment, there is provided anon-transitory computer-readable recording medium includes a program,which, when executed by a computer, performs any one of the methods.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of the embodiments, taken inconjunction with the accompanying drawings in which:

FIG. 1 is a block diagram illustrating a hardware configuration of aneural network apparatus according to an embodiment;

FIG. 2 is a diagram illustrating a computation performed by a deepneural network (DNN) according to an embodiment;

FIG. 3 is a diagram illustrating a computation performed by a recurrentneural network (RNN) according to an embodiment;

FIG. 4 is a conceptual diagram illustrating a neural network system forgenerating a chemical structure according to an embodiment;

FIG. 5 is a diagram illustrating a method of representing a chemicalstructure, according to an embodiment;

FIG. 6 is a diagram illustrating a method of interpreting a neuralnetwork, according to an embodiment;

FIG. 7 is a diagram illustrating an example of changing an expressionregion of a descriptor to generate a new chemical structure according toan embodiment;

FIG. 8 is a diagram illustrating an example of changing a partialstructure by changing a bit value of a descriptor according to anembodiment;

FIG. 9 is a diagram illustrating an example of changing a partialstructure by changing a pixel value of an image according to anembodiment;

FIG. 10 is a diagram illustrating an example of changing a pixel valuewhen there are a plurality of expression regions on an image accordingto an embodiment;

FIG. 11 is a flowchart of a method of generating a new chemicalstructure by changing a descriptor for a chemical structure in a neuralnetwork apparatus, according to an embodiment; and

FIG. 12 is a flowchart of a method of generating a new chemicalstructure by changing an image for a chemical structure in a neuralnetwork, apparatus according to an embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings, wherein like referencenumerals refer to like elements throughout. In this regard, the presentembodiments may have different forms and should not be construed asbeing limited to the descriptions set forth herein. Accordingly, theembodiments are merely described below, by referring to the figures, toexplain aspects. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items. Expressionssuch as “at least one of,” when preceding a list of elements, modify theentire list of elements and do not modify the individual elements of thelist such that expressions of or similar to “at least one of a, b, andc” include: only a, only b, only c, only a and b, only b and c, only aand c, and all of a, b, and c.

The terms “according to some embodiments” or “according to anembodiment” used throughout the specification do not necessarilyindicate the same embodiment.

Some embodiments of the disclosure may be represented by functionalblock configurations and various processing operations. Some or all ofthese functional blocks may be implemented using various numbers ofhardware and/or software components that perform particular functions.For example, the functional blocks of the disclosure may be implementedusing one or more microprocessors or circuits executing instructions toperform a given function. Also, for example, the functional blocks ofthe disclosure may be implemented in various programming or scriptinglanguages. The functional blocks may be implemented with algorithmsexecuted by one or more processors. The disclosure may also employconventional techniques for electronic configuration, signal processing,and/or data processing. The terms “mechanism”, “element”, “unit” and“configuration” may be used in a broad sense and are not limited tomechanical and physical configurations.

Also, connection lines or connection members between the componentsillustrated in the drawings are merely illustrative of functionalconnections and/or physical or circuit connections. In actual devices,connections between the components may be represented by variousfunctional connections, physical connections, or circuit connectionsthat may be replaced or added.

Meanwhile, with respect to the terms used herein, a descriptor that isdata used in a neural network system refers to an indicator value usedto describe structural characteristics of a chemical structure and maybe acquired by performing a relatively simple computation on a givenchemical structure. According to an embodiment, a descriptor may includea molecular structure fingerprint indicating whether a particularpartial structure is included (e.g., Morgan fingerprint and extendedconnectivity fingerprint (ECFP)). Also, the descriptor may be aquantitative structure-property relationship (QSPR) model configuredwith a value that may be immediately calculated from a given chemicalstructure, for example, a molecular weight or the number of partialstructures (e.g., rings) included in a molecular structure.

In addition, a property refers to a characteristic possessed by achemical structure and may be a real number value measured by anexperiment or calculated by a simulation. For example, when the chemicalstructure is used as a display material, the property of the chemicalstructure may be expressed by a transmission wavelength, an emissionwavelength, or the like with respect to light. When the chemicalstructure is used as a battery material, the property of the chemicalstructure may be a voltage. Unlike the descriptor, calculation of theproperty may require complex simulations that necessitate additionalcalculation and computation beyond similar simulations for thedescriptor.

Also, a structure refers to an atomic level structure of a chemicalstructure. In order to derive a property by performing First PrinciplesCalculation, the structure is required to be expressed at an atomiclevel. Thus, an atomic level structure needs to be derived to generate anovel chemical structure. The structure may be a structural formulabased on atomic bonding relationships or a character string in a simpleformat (one-dimensional). The format of the character string expressingthe structure may be a Simplified Molecular-input Line-entry System(SMILES) code, a Smiles Arbitrary Target Specification (SMARTS) code, anInternational Chemical Identifier (InChi) code, or the like.

In addition, a factor refers to an element defining the relationshipsamong the descriptor, the property, and the structure. The factor may bedetermined by machine learning based on a descriptor-property-structuralformula stored in a database. Thus, how relationships between thefactor, the descriptor, the property, and the structural formula may bedetermined.

Hereinafter, embodiments of the disclosure will be described in detailwith reference to the accompanying drawings.

FIG. 1 is a block diagram illustrating a hardware configuration of aneural network apparatus 100 according to an embodiment.

The neural network apparatus 100 may be implemented using various typesof devices such as a personal computer (PC), a server, a mobile device,and an embedded device. Examples of the neural network apparatus 100 mayinclude, but are not limited to, a smartphone, a tablet device, anaugmented reality (AR) device, an Internet of Things (IoT) device, anautonomous vehicle, a robot, a medical device, and the like whichperform speech recognition, image recognition, image classification, andthe like using a neural network. Furthermore, the neural networkapparatus 100 may be a dedicated hardware (HW) accelerator mounted on,connected to, or installed in the devices described above. The neuralnetwork apparatus 100 may be a hardware accelerator such as a neuralprocessing unit (NPU), a tensor processing unit (TPU), or a neuralengine, which are dedicated modules for driving a neural network, but isnot limited thereto.

Referring to FIG. 1, the neural network apparatus 100 includes aprocessor 110 and a memory 120. FIG. 1 only illustrates components ofthe neural network apparatus 100 related to the embodiments of thedisclosure. Thus, it is apparent to those skilled in the art that theneural network apparatus 100 may further include any othergeneral-purpose components in addition to the components shown in FIG.1.

The processor 110 controls the overall function for driving the neuralnetwork apparatus 100. For example, the processor 110 controls theoverall operation of the neural network apparatus 100 by executingprograms stored in the memory 120 of the neural network apparatus 100.The processor 110 may be implemented as a central processing unit (CPU),a graphics processing unit (GPU), an application processor (AP), or thelike provided in the neural network apparatus 100, but is not limitedthereto.

The memory 120 is a hardware component that stores a variety of dataprocessed in the neural network apparatus 100. For example, the memory120 may store data processed and to be processed by the neural networkapparatus 100. The memory 120 may also store applications, drivers, andthe like to be executed by the processor 110 of the neural networkapparatus 100. The memory 120 may include random access memory (RAM)such as dynamic random access memory (DRAM) and static random accessmemory (SRAM), read-only memory (ROM), electrically erasableprogrammable read-only memory (EEPROM), a CD-ROM, Blue-ray or otheroptical disk storage, a hard disk drive (HDD), a solid state drive(SSD), or a flash memory.

The memory 120 may store a descriptor for a chemical structure and aproperty value numerically representing the property of the chemicalstructure, which match each other as one set or pair. The neural networkapparatus 100 may read the descriptor and the property valuecorresponding thereto from the memory 120 or write the descriptor andthe property value corresponding thereto in the memory 120. In anembodiment, the descriptor may include a plurality of bit values and theproperty value may be a value for a transmission wavelength, an emissionwavelength, a voltage, or the like.

Although not shown in FIG. 1, the memory 120 may store an image for achemical structure and a property value numerically representing theproperty of the chemical structure, which are associated with each otheras one set or pair. In an embodiment, the image may include n×m pixels(where n and m are natural numbers). Hereinafter, the description of thedescriptor applies equally to a case where the descriptor is replacedwith the image.

The memory 120 may store a structure characteristic value representing achemical structure and a descriptor and a property value, which matchthe structure characteristic value as one set or pair. The structurecharacteristic value may be a SMILES code or a SMARTS code, as a stringformat that expresses a chemical structure.

The processor 110 may execute instructions to implement an artificialneural network (ANN), such as a deep neural network (DNN) and arecurrent neural network (RNN).

The processor 110 may allow the DNN to learn by using a descriptor and aproperty value corresponding to the descriptor and may determine afactor defining the relationship between the descriptor and the propertyvalue in this process. Then, the processor 110 may output a propertyvalue corresponding to a new descriptor as output data by driving thetrained DNN by using, as input data, the new descriptor not used in thelearning process of the DNN.

The processor 110 may allow the RNN to learn by using a descriptor and astructure characteristic value and may determine a factor defining therelationship between the descriptor and the structure characteristicvalue in this process. Then, the processor 110 may output a structurecharacteristic value corresponding to a new descriptor as output data bydriving the trained RNN by using, as input data, the new descriptor notused in the learning process of the RNN.

The neural network apparatus 100 may further include a user interface.The user interface refers to a device or software used to input data tocontrol the neural network apparatus 100. Examples of the user interfacemay include, but are not limited to, a key pad, a dome switch, a touchpad (e.g., capacitive overlay type, resistive overlay type, infraredbeam type, surface acoustic wave type, integral strain gauge type, andpiezo electric type), a jog wheel, and a jog switch, along with agraphical user interface (GUI) that may be displayed for receiving userinput.

FIG. 2 is a diagram illustrating a computation performed by a DNNaccording to an embodiment.

Referring to FIG. 2, a DNN 20 may have a structure including an inputlayer, hidden layers, and an output layer, perform a computation basedon received input data (e.g., I₁ and I₂), and generate output data(e.g., O₁ and O₂) based on a computation result.

For example, as illustrated in FIG. 2, the DNN 20 may include an inputlayer (Layer 1), two hidden layers (Layer 2 and Layer 3), and an outputlayer (Layer 4). Because the DNN 20 may include many layers to processvalid information, the DNN 20 may process complex data compared to aneural network including a single layer. Meanwhile, although the DNN 20illustrated in FIG. 2 includes 4 layers, the DNN 20 is only an exampleand may also include more or fewer layers and more or fewer channelsthan those illustrated therein. That is, the DNN 20 may have variousstructures of layers different from that illustrated in FIG. 2.

Each of the layers included in the DNN 20 may have a plurality ofchannels. The channels may respectively correspond to a plurality ofartificial nodes known as neurons, processing elements (PEs), units, orsimilar terms. For example, as illustrated in FIG. 2, Layer 1 mayinclude two channels (nodes), and Layers 2 and 3 may include threechannels respectively. However, the layers are only examples and each ofthe layers included in the DNN 20 may have various numbers of channels(nodes) and interconnections to other nodes.

The channels included in each of the layers of the DNN 20 may beinterconnected to process data. For example, a channel may perform acomputation of data received from channels of one layer and output acomputation result to channels of another layer.

Input and output of each channel may be referred to as input activationand output activation. That is, activation may be not only an output ofone channel but also a parameter corresponding to an input of channelsincluded in a successive layer. Meanwhile, each of the channels maydetermine an activation thereof based on activations and weightsreceived from channels included in a previous layer. The weight is aparameter used to calculate the output activation of each channel andmay be a value assigned to the relationship between channels.

Each of the channels may be processed by a computational unit or aprocessing element that receives an input and generates an outputactivation. The input-output of each channel may be mapped. For example,when σ is an activation function, w_(jk) ^(i) is a weight from a k^(th)channel included in an (i−1)^(th) layer to a j^(th) channel included inan i^(th) layer, b_(j) ^(i) is a bias of the j^(th) channel included inthe i^(th) layer, and is an activation of the j^(th) channel of thei^(th) layer, an activation a_(j) ^(j) may be calculated using Equation1 below.

$\begin{matrix}{a_{j}^{i} = {\sigma\left( {{\sum\limits_{k}\left( {w_{jk}^{i} \times a_{k}^{i - 1}} \right)} + b_{j}^{i}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

As illustrated in FIG. 2, an activation of a first channel CH1 of asecond layer Layer 2 may be expressed as a₁ ². In addition, a₁ ² mayhave a value of a₁ ²=σ(w_(1,1) ²×a₁ ¹+w_(1,2) ²×a₂ ¹+b₁ ²) according toEquation 1. In Equation 1, σ denotes an activation function such asRelu, sigmoid, and tanh. As a result, the activation of a particularchannel in a particular layer may denote a result obtained by passing avalue of Σ(w_(jk) ^(i)×a_(k) ^(i-1))+b_(j) ^(i) through the activationfunction.

However, the above-described Equation 1 is only an example fordescribing the activation and the weight used to process data in the DNN20 and the embodiment is not limited thereto.

In an embodiment, the neural network apparatus 100 may allow the DNN 20to learn by using a descriptor (or image) and a property value, storedin a memory. The DNN 20 may determine a factor defining the relationshipbetween a descriptor (or image) and a property value in a learningprocess using the descriptor and the property value.

That is, among Layers 1 to 4 constituting the DNN 20, the descriptor (orimage) may correspond to the values of a plurality of channels (nodes)of the input layer (Layer 1), the property value may correspond to thevalues of a plurality of channels (nodes) of the output layer (Layer 4),and the factor may correspond to the values of a plurality of channels(nodes) of at least one hidden layer (Layers 2 and/or 3).

Then, the trained DNN 20 may be driven by receiving a new descriptor (ornew image) as input data and thus may output a property valuecorresponding to the received new descriptor (or new image) as outputdata.

FIG. 3 is a diagram illustrating a computation performed by an RNNaccording to an embodiment.

Hereinafter, descriptions given above with reference to FIG. 2 will notbe repeated for descriptive convenience.

An RNN 30 is a neural network that analyzes data changing with time suchas time-series data and constructed by connecting a network between areference time point t and a next time point t+1. That is, the RNN 30 isa neural network in which a temporal aspect is considered and capable ofeffectively learning a pattern from data sequentially input or datainput with a sequence of features by modifying a model to allow arecursive input to a hidden layer of the neural network.

Referring to FIG. 3, a node s constituting a hidden layer of the RNN 30is illustrated. The node s may perform a computation based on input datax and generate output data o. The RNN 30 may iteratively apply the sametask to all sequences and a final output result of the node s isaffected by a result of a previous calculation.

An RNN 31 is an unfolded RNN 30 with a loop. The term “unfold” withrespect to the RNN 30 refers to expressing the RNN 30 for the entiresequence. In the RNN 31, x_(t) is an input value at a time step t, ands_(t) is a hidden state at the time step t. The term s_(t) may beexpressed by Equation 2 below. In Equation 2, a tanh or Relu functionmay be used as function f. The term s⁻¹ to calculate a first hiddenstate may generally be initialized to 0. In addition, in the RNN 31, ofis an output value at the time step t.

s _(t) =f(U _(x) _(t) +W _(s) _(t-1) )  [Equation 2]

Here, s_(t) is a memory portion of the network and stores information onevents at previous time steps. The output value of depends only on thememory of the current time step t.

Meanwhile, unlike the existing neural network structure in which theparameters are different from each other, the RNN 31 shares theparameters U, V, and W for all time steps. That is, because each step ofthe RNN 31 performs almost the same calculation except for an inputvalue, the number of parameters to be learned may be reduced.

In an embodiment, the neural network apparatus 100 may allow the RNN 31to learn by using a descriptor (or image) and a property value, storedin a memory. Alternatively, the neural network apparatus 100 may allowthe RNN 31 to learn by using a factor and a property value, determinedin a learning process of the DNN 20.

For example, when W of the RNN 31 is a factor determined in a learningprocess of the DNN 20 and a structure characteristic value representedby a SMILES code is “ABCD”, O_(t−1) and x_(t) may be “ABC”, and O_(t)and x_(t+1) may be “BCD”. Then, a SMILES code in each time step may beaggregated to output one SMILES code “ABCDEFG”, i.e., a structurecharacteristic value, as output data.

Then, the trained RNN 31 may be driven by receiving a new descriptor (ornew image) as input data and thus may output a structure characteristicvalue corresponding to the received new descriptor (or new image) asoutput data. Alternatively, the trained RNN 31 may be driven byreceiving a factor for a new descriptor (or new image) as input data andthus may output a structure characteristic value corresponding to thenew descriptor (or new image) as output data.

FIG. 4 is a conceptual diagram illustrating a neural network system forgenerating a chemical structure according to an embodiment.

Referring to FIG. 4, a neural network system configured to generate achemical structure by using a DNN 410 and an RNN 420 is illustrated.

A descriptor that is data used in the neural network system may berepresented by an ECFP as an indicator value used to representstructural characteristics of a chemical structure. A property refers toa characteristic possessed by a chemical structure and may be a realnumber value indicating a transmission wavelength and an emissionwavelength with respect to light. A structure refers to an atomic levelstructure of a chemical structure and may be represented by a SMILEScode. For example, a structural formula may be expressed according to aSMILES code, as shown in Equation 3 below.

OC1=C(C═C2C═CNC2=C1)C1=C(C═CC═C1)C1=CC2=C(NC═C2)C═C1[Equation 3]

The factor is an element defining the relationships among thedescriptor, the property, and the structure. The factor may be at leastone hidden layer. When the factor includes a plurality of hidden layers,a factor defining the relationship between the descriptor and theproperty, a factor defining the relationship between the descriptor andthe structure, and the like may be determined for each hidden layer.

The DNN 410 may be driven by receiving a descriptor as input data andoutput a property value corresponding to the received descriptor asoutput data. In a learning process using a descriptor and a propertyvalue, the DNN 410 may determine a factor defining the relationshipbetween the descriptor and the property value. The RNN 420 may be drivenby receiving a descriptor or a factor determined in a learning processof the DNN 410 as input data and output a structure characteristic valueas output data.

FIG. 5 is a diagram illustrating a method of representing a chemicalstructure 510, according to an embodiment.

Referring to FIG. 5, the chemical structure 510 represents the shape ofa molecule formed by a combination of atoms. The chemical structure 510may be represented by the position of an atom, the distance betweenatoms, the strength of an atomic bond, and the like.

In an embodiment, the chemical structure 510 may be represented by adescriptor 520 including a plurality of bit values (1 or 0). It may bedetermined whether the chemical structure 510 includes a particularpartial structure, through the descriptor 520.

In another embodiment, the chemical structure 510 may be represented asan image 530 having a certain size. The image 530 may include threechannels (red, green, and blue (RGB)) of n×m pixels (where n and m arenatural numbers). In the image, 8 bits, i.e., a value from 0 (black) to255 (white), may be assigned to each pixel of the image 530. Forexample, bright red may be synthesized with an R channel value of 246, aG channel value of 20, and a B channel value of 50, and when all channelvalues are 255, white is synthesized.

Hereinafter, for convenience of description, a method in which thechemical structure 510 is displayed as an image 530 by using one channelwill be described.

Atoms constituting the chemical structure 510 may be displayed in colorsthat are distinct from each other on the image 530. The chemicalstructure 510 may include carbon (C), nitrogen (N), and oxygen (O), andon the image 530, the carbon (C) may be displayed in black, the nitrogen(N) may be displayed in blue, and the oxygen (O) may be displayed inred.

Referring to FIG. 5, on the image 530 including 6×6 pixels, the value ofa pixel at which the carbon (C) of the chemical structure 510 is locatedmay be ‘0’, the value of a pixel at which the nitrogen (N) is locatedmay be ‘50’, and the value of a pixel at which the oxygen (O) is locatedmay be ‘186’. The value of a pixel where no atom is present may be‘255’.

The color type in which a certain atom is displayed on the image 530,the number of pixels constituting the image 530, and the like are notlimited to the above example.

The descriptor 520 or the image 530 for the chemical structure 510 maybe used as input data of a neural network, and a particular propertyvalue for the chemical structure 510 may be output as output data of theneural network.

FIG. 6 is a diagram illustrating a method of interpreting a neuralnetwork, according to an embodiment.

The neural network apparatus 100 may obtain a descriptor or image for areference chemical structure to output a particular property value forthe reference chemical structure. In an embodiment, the descriptor mayinclude a plurality of bit values, and the image may include n×m pixels(where n and m are natural numbers).

The neural network apparatus 100 may input the descriptor or image forthe reference chemical structure to the trained neural network as inputdata and drive the neural network, through an inference process 610, toobtain a particular property value for the reference chemical structureas output data of the neural network.

In this case, the neural network apparatus 100 may perform aninterpretation process 620 to determine whether a particular propertyvalue is expressed by a partial structure in the reference chemicalstructure.

Referring to FIG. 6, in an embodiment, the neural network apparatus 100may interpret a trained neural network by using a Layer-wise RelevancePropagation (LRP) technique. The LRP technique is a method ofpropagating relevance in a reverse direction (i.e., a direction from anoutput layer to an input layer) of the trained neural network. In theLRP technique, when the relevance is propagated between layers, a nodehaving the greatest relevance to an upper layer among a plurality ofnodes of a lower layer obtains the greatest relevance from thecorresponding node of the upper layer.

A method of calculating relevance in the LRP technique may be expressedby Equation 4. In Equation 4, as and a_(j) are an output value to bedetermined in a particular node of an i^(th) layer and an output valueto be determined in a particular node of a j^(th) layer, respectively.w⁺ _(ij) is a weight value that connects the particular node of thei^(th) layer to the particular node of the j^(th) layer. R_(i) and R_(j)denote the relevance of the particular node of the i^(th) layer and therelevance of the particular node of the j^(th) layer, respectively.

$\begin{matrix}{R_{i} = {\sum\limits_{j}{\frac{a_{i}w_{ij}^{+}}{\sum\limits_{i}{a_{j}w_{ij}^{+}}}R_{j}}}} & \left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack\end{matrix}$

In an embodiment, for the application of the LRP technique, the neuralnetwork apparatus 100 may designate an activation function, applied to anode of a trained neural network, as a linear function by using aregression analysis method, and may designate Mean Square Error (MSE)for optimization. Specifically, in the regression analysis method,because a final output value may include several integer values, theneural network may be trained by designating an activation function ofan output node as a linear function. In order to implement theregression analysis method, a loss function may be designated as MSE ina neural network learning process.

However, the technique that may be used in the interpretation process620 to determine whether a particular property value is expressed by anypartial structure in the reference chemical structure is not limited tothe example described above.

When the input data of the neural network is a descriptor for thereference chemical structure, a plurality of nodes of the input layermay respectively correspond to bit values constituting the descriptor.The neural network apparatus 100 may obtain a node of the input layer(i.e., a bit position of the descriptor), which has the greatestrelevance to the expression of a particular property value of thereference chemical structure, through the interpretation process 620.Because the bit position of the descriptor corresponds to a particularpartial structure in the reference chemical structure, the neuralnetwork apparatus 100 may determine a particular partial structure,which has the greatest relevance to the expression of a particularproperty value of the reference chemical structure, by obtaining the bitposition of the descriptor through the interpretation process 620.

When the input data of the neural network is an image for the referencechemical structure, the plurality of nodes of the input layer mayrespectively correspond to pixel values constituting the image. Theneural network apparatus 100 may obtain a node of the input layer (i.e.,pixel coordinates of the image), which has the greatest relevance to theexpression of a particular property value of the reference chemicalstructure, through the interpretation process 620. Because the pixelcoordinates of the image corresponds to a particular partial structurein the reference chemical structure, the neural network apparatus 100may determine a particular partial structure, which has the greatestrelevance to the expression of a particular property value of thereference chemical structure, by obtaining the pixel coordinates of theimage through the interpretation process 620.

Hereinafter, the bit position of the descriptor and the pixelcoordinates of the image, which have the greatest relevance to theexpression of a particular property value of the reference chemicalstructure, will be referred to as an expression region.

FIG. 7 is a diagram illustrating an example of changing an expressionregion of a descriptor to generate a new chemical structure according toan embodiment.

Referring to FIG. 7, a descriptor 712 of a reference chemical structure710 may be ‘11100011011010110’. The neural network apparatus 100 maysequentially input bit values constituting the descriptor 712respectively to nodes of an input layer of the neural network (e.g.,DNN) and output a property value (i.e., an ‘emission wavelength: 320nm’) for the reference chemical structure 710.

The neural network apparatus 100 may obtain a node of the input layer(i.e., an expression region 713 of the descriptor 712), which has thegreatest relevance to the expression of a wavelength value of thereference chemical structure 710. The expression region 713 of thedescriptor 712 may correspond to a particular position 711 in thereference chemical structure 710. In FIG. 7, the expression region 713corresponds to one bit value. However, the expression region 713 maycorrespond to a plurality of consecutive bit values, and a plurality ofexpression regions may be in the descriptor 712.

The neural network apparatus 100 may change a bit value of theexpression region 713 to improve the property of the reference chemicalstructure 710. The structure of the particular position 711 may bechanged as the bit value of the expression region 713 is changed. As amethod of changing the bit value of the expression region 713, agenetics algorithm may be used, and the details thereof will bedescribed later with reference to FIG. 8. The neural network apparatus100 may change the bit value of the expression region 713 and/or a bitvalue around the expression region 713.

The neural network apparatus 100 may change the bit value of theexpression region 713 and output a new descriptor 722. Referring to FIG.7, as a bit value ‘1’ of the expression region 713 in the new descriptor722 is changed to ‘110100’, the neural network apparatus 100 may apply anew partial structure 721 corresponding to the bit value ‘110100’ to theparticular position 711 and generate a new chemical structure 720 towhich the new partial structure 721 is applied.

In connection with a method of generating the new chemical structure720, the neural network apparatus 100 may input the new descriptor 722as input data of a neural network (e.g., RNN) and output a structurecharacteristic value as output data, and may generate the new chemicalstructure 720 based on the output structure characteristic value.

The neural network apparatus 100 may input the descriptor 722 of the newchemical structure 720 into the neural network and output a propertyvalue (i.e., ‘emission wavelength: 325 nm’) corresponding to thedescriptor 722 input into the neural network. That is, the neuralnetwork apparatus 100 may improve a property by changing a partialstructure of the reference chemical structure 710 and generating the newchemical structure 720.

The neural network apparatus 100 may repeatedly generate a chemicalstructure through the above-described process until a chemical structurehaving a property value close to a preset value (e.g., ‘emissionwavelength: 350 nm’) is generated.

Specifically, the neural network apparatus 100 may compare a propertyvalue (e.g., ‘emission wavelength: 325 nm’) for the new chemicalstructure 720 to a preset value (e.g., ‘emission wavelength: 350 nm’)and generate a new chemical structure by changing the bit value of theexpression region 723 of the descriptor 722 when the property value forthe new chemical structure 720 is less than the preset value.

When the property value for the new chemical structure generated throughthe above-described process is equal to or greater than the presetvalue, the neural network apparatus 100 may store the generated newchemical structure in a memory.

FIG. 8 is a diagram illustrating an example of changing a partialstructure by changing a bit value of a descriptor according to anembodiment.

In an embodiment, the neural network apparatus 100 may apply a geneticsalgorithm to a bit value constituting a descriptor of a referencechemical structure and perform a selection, intersection, or mutationoperation on the bit value.

The neural network apparatus 100 may change the descriptor of thereference chemical structure by applying the genetics algorithm to thebit value constituting the descriptor of the reference chemicalstructure. As the descriptor of the reference chemical structure ischanged or modified, a partial structure in the reference chemicalstructure may be mutated, removed, or replaced, or a partial structuremay be added to the reference chemical structure.

Referring to FIG. 8, the neural network apparatus 100 may mutate thepartial structure in the reference chemical structure by applying thegenetics algorithm to the bit value constituting the descriptor of thereference chemical structure. For example, the neural network apparatus100 may change carbon (C) in a first position 810 in the referencechemical structure to nitrogen (N). Alternatively, the neural networkapparatus 100 may change adjacent atoms 811 and 812 combined with anatom in the first position 810 to other atoms.

In addition, the neural network apparatus 100 may add a partialstructure to the reference chemical structure by applying the geneticsalgorithm to the bit value constituting the descriptor of the referencechemical structure. For example, the neural network apparatus 100 mayadd a partial structure 823 to be connected to an atom in a secondposition 820 in the reference chemical structure. Alternatively, theneural network apparatus 100 may add a partial structure to be connectedto adjacent atoms 821 and 822 combined with an atom in the secondposition 820. Alternatively, the neural network apparatus 100 may add apartial structure 824 in the form of a condensed ring, connected to bothan atom in the second position 820 and an adjacent atom 821 combinedwith the atom in the second position 820.

In addition, the neural network apparatus 100 may remove a partialstructure in the reference chemical structure by applying the geneticsalgorithm to the bit value constituting the descriptor of the referencechemical structure. For example, the neural network apparatus 100 mayremove a partial structure 831 connected to an atom in a third position830 in the reference chemical structure. Alternatively, the neuralnetwork apparatus 100 may change a ring structure by removing an atom inthe third position 830.

In addition, the neural network apparatus 100 may replace a partialstructure in the reference chemical structure by applying the geneticsalgorithm to the bit value constituting the descriptor of the referencechemical structure. For example, the neural network apparatus 100 maychange a ring structure of the fourth position 840 in the referencechemical structure to a new partial structure 841 or 842.

However, the example of changing a partial structure by changing the bitvalue of the descriptor is not limited to the above descriptions.

FIG. 9 is a diagram illustrating an example of changing a partialstructure by changing a pixel value of an image according to anembodiment.

Referring to FIG. 9, an image 912 of a reference chemical structure 910may include 6×6 pixels. Atoms constituting the reference chemicalstructure 910 may be displayed in colors that are distinct from eachother on the image 912. The reference chemical structure 910 may includecarbon (C), nitrogen (N), and oxygen (O), and on the image 912, thecarbon (C) may be displayed in black, the nitrogen (N) may be displayedin blue, and the oxygen (O) may be displayed in red. On the image 912,the value of a pixel at which the carbon (C) is located may be ‘0’, thevalue of a pixel at which the nitrogen (N) is located may be ‘50’, andthe value of a pixel at which the oxygen (O) is located may be ‘186’.

The neural network apparatus 100 may sequentially input pixel valuesconstituting the image 912 respectively to nodes of an input layer of aneural network (e.g., DNN) and thus may output a property value (i.e.,an ‘emission wavelength: 320 nm’) for the reference chemical structure910.

The neural network apparatus 100 may obtain a node of the input layer(i.e., an expression region 913 of the image 912) which has the greatestrelevance to the expression of a wavelength value of the referencechemical structure 910. The expression region 913 of the image 912 maycorrespond to a particular position 911 in the reference chemicalstructure 910. In FIG. 9, the expression region 913 corresponds to onepixel value. However, the expression region 913 may correspond to aplurality of adjacent pixel values, and a plurality of expressionregions may be provided in the image 912.

The neural network apparatus 100 may change a pixel value of theexpression region 913 and/or a pixel value around the expression region913 to improve the property of the reference chemical structure 910. Thestructure of the particular position 911 may be changed as the pixelvalue of the expression region 913 and/or the pixel value around theexpression region 913 are changed. In an embodiment, the pixel value ofthe expression region 913 and/or the pixel values around the expressionregion 913 may be changed by using Gaussian noise. Gaussian noise refersto a noise of which a distribution function of an arbitrary order isrepresented by a normal distribution.

The neural network apparatus 100 may change the pixel value of theexpression region 913 and/or the pixel value around the expressionregion 913 and thus output a new image 922. Referring to FIG. 9, as thepixel value of the expression region 913 and/or the pixel values aroundthe expression region 913 in the new image 922 are changed, the neuralnetwork apparatus 100 may apply a new partial structure 921corresponding to a changed pixel value to the particular position 911and generate a new chemical structure 920.

In connection with a method of generating the new chemical structure920, the neural network apparatus 100 may input the new image 922 asinput data of a neural network (e.g., RNN) and output a structurecharacteristic value as output data, and may generate the new chemicalstructure 920 based on the output structure characteristic value.

The neural network apparatus 100 may input the image 922 of the newchemical structure 920 into the neural network and output a propertyvalue (i.e., ‘emission wavelength: 325 nm’) corresponding to the image922 input into the neural network. That is, the neural network apparatus100 may improve a property by changing a partial structure of thereference chemical structure 910 and generating the new chemicalstructure 920.

The neural network apparatus 100 may repeatedly generate a chemicalstructure through the above-described process until a chemical structurehaving a property value close to a preset value (e.g., ‘emissionwavelength: 350 nm’) is generated.

Specifically, the neural network apparatus 100 may compare a propertyvalue (e.g., ‘emission wavelength: 325 nm’) for the new chemicalstructure 920 to a preset value (e.g., ‘emission wavelength: 350 nm’)and generate a new chemical structure by changing the pixel value of theexpression region 913 and the pixel value around the expression region913 in the image 922 when the property value for the new chemicalstructure 920 is less than the preset value.

When the property value for the new chemical structure generated throughthe above-described process is equal to or greater than the presetvalue, the neural network apparatus 100 may store the generated newchemical structure in a memory.

FIG. 10 is a diagram illustrating an example of changing a pixel valuewhen there are a plurality of expression regions on an image accordingto an embodiment.

Referring to FIG. 10, an image 1012 of a reference chemical structure1010 may include 6×6 pixels. Atoms constituting the reference chemicalstructure 1010 may be displayed in colors that are distinct from eachother on the image 1012. For example, on the image 1012, the value of apixel at which the carbon (C) is located may be ‘0’, the value of apixel at which the nitrogen (N) is located may be ‘50’, and the value ofa pixel at which the oxygen (O) is located may be ‘186’.

The neural network apparatus 100 may sequentially input pixel valuesconstituting the image 1012 to nodes of an input layer of a neuralnetwork (e.g., DNN) and output a property value (i.e., an ‘emissionwavelength: 320 nm’) for the reference chemical structure 1010.

In an embodiment, there may be multiple nodes of an input layer thathave the greatest relevance, or high relevance with respect to othernodes, to the expression of a wavelength value of the reference chemicalstructure 1010. That is, there may be a plurality of expression regions,i.e., a first expression region 1013 a and a second expression region1013 b, on the image 1012. The first expression region 1013 a and thesecond expression region 1013 b in the image 1012 may correspond to afirst position 1011 a and a second position 1011 b in the referencechemical structure 1010, respectively. As shown in FIG. 10, the secondposition 1011 b corresponding to the second expression region 1013 b maybe outside the reference chemical structure 1010.

When there are a plurality of expression regions, i.e., the firstexpression region 1013 a and the second expression region 1013 b, on theimage 1012, the neural network apparatus 100 may obtain coordinateinformation on the image 1012, which corresponds to the plurality ofexpression regions, i.e., the first expression region 1013 a and thesecond expression region 1013 b. For example, based on a lower leftcorner of the image 1012 having the origin (0,0), the coordinateinformation of the first expression region 1013 a may be (3, 3) and thecoordinate information of the second expression region 1013 b may be (5,3).

The neural network apparatus 100 may output the coordinate information(4, 3) of a center point 1014 based on the coordinate informationcorresponding to the plurality of expression regions, i.e., the firstexpression region 1013 a and the second expression region 1013 b. Theneural network apparatus 100 may change a pixel value of the centerpoint 1014 and/or a pixel value around the center point 1014 to improvethe property of the reference chemical structure 1010. As the pixelvalue of the center point 1014 and/or the pixel value around the centerpoint 1014 are changed, the structure of a particular position 1015 onthe reference chemical structure 1010 corresponding to the center point1014 may be changed. In an embodiment, the pixel value of the centerpoint 1014 and/or the pixel value around the center point 1014 may bechanged by using Gaussian noise.

The neural network apparatus 100 may change the pixel value of thecenter point 1014 and/or the pixel value around the center point 1014and output a new image 1022. Referring to FIG. 10, as the pixel value ofthe center point 1014 and/or the pixel value around the center point1014 in the new image 1022 are changed, the neural network apparatus 100may apply a new partial structure 1021 corresponding to a changed pixelvalue to the particular position 1015 and generate a new chemicalstructure 1020.

The neural network apparatus 100 may input the image 1022 of the newchemical structure 1020 into the neural network and output a propertyvalue (i.e., ‘emission wavelength: 325 nm’) corresponding to the image1022 input into the neural network. That is, the neural networkapparatus 100 may improve a property by changing a partial structure ofthe reference chemical structure 1010 and generating the new chemicalstructure 1020.

FIG. 11 is a flowchart of a method of generating a new chemicalstructure by changing a descriptor for a chemical structure in a neuralnetwork apparatus according to an embodiment.

The method of generating a chemical structure in a neural networkapparatus relates to the embodiments described above with reference tothe drawings, and thus, although omitted in the following descriptions,descriptions given above with reference to the drawings may also beapplied to the method illustrated in FIG. 11.

Referring to FIG. 11, in operation 1110, the neural network apparatusmay obtain a descriptor for a reference chemical structure.

The descriptor is an indicator value used to represent the structuralcharacteristics of a chemical structure. The descriptor may be obtainedby performing a relatively simple operation on a given chemicalstructure. In an embodiment, the descriptor may be represented by anECFP and may include a plurality of bit values. However, the manner ofexpression of the descriptor is not limited thereto.

Hereinafter, the descriptor for the reference chemical structure will bereferred to as a reference descriptor.

In operation 1120, the neural network apparatus may input a referencedescriptor into a trained neural network and output a property value ofa particular property for the reference chemical structure.

The property refers to a characteristic possessed by a chemicalstructure and may be a real number value indicating a transmissionwavelength and an emission wavelength with respect to light. Unlike thecase of the descriptor, the computation of the property may requirecomplex simulations and be time consuming.

A memory of the neural network apparatus may store a descriptor for aparticular chemical structure and a property value numericallyrepresenting the property of the particular chemical structure, whichmatch each other as one set.

In an embodiment, the neural network apparatus may allow a neuralnetwork (e.g., DNN) to learn by using a descriptor and a property value,stored in a memory. In a learning process using the descriptor and theproperty value, a factor defining the relationship between thedescriptor and the property value may be determined in the neuralnetwork.

The neural network apparatus may output a property value correspondingto the reference descriptor as output data of the neural network byinputting the reference descriptor as input data of the trained neuralnetwork and driving the neural network.

In operation 1130, the neural network apparatus may determine anexpression region that expresses a particular property in the referencedescriptor.

The neural network apparatus may perform an interpretation process todetermine whether a particular property value is expressed by anypartial structure in the reference chemical structure.

In an embodiment, the neural network apparatus may interpret the trainedneural network by using an LRP technique. The LRP technique is a methodof propagating relevance in a reverse direction (i.e., a direction froman output layer to an input layer) of the trained neural network. In theLRP technique, when the relevance is propagated between layers, a nodehaving the greatest relevance to an upper layer among a plurality ofnodes of a lower layer obtains the greatest relevance from thecorresponding node of the upper layer.

For the application of the LRP technique, the neural network apparatusmay designate an activation function, applied to a node of the trainedneural network, as a linear function, and may designate MSE foroptimization.

A plurality of nodes of the input layer of the neural network mayrespectively correspond to bit values constituting the descriptor. Theneural network apparatus may obtain a node of the input layer, i.e., abit position (or expression region) of the reference descriptor, whichhas the greatest relevance in the expression of a particular propertyvalue of the reference chemical structure, through the interpretationprocess. Because the expression region of the reference descriptorcorresponds to a particular partial structure in the reference chemicalstructure, the neural network apparatus may determine a particularpartial structure, which has the greatest relevance in the expression ofa particular property value of the reference chemical structure, byobtaining the expression region of the reference descriptor through theinterpretation process.

In operation 1140, the neural network apparatus may generate a newchemical structure by changing a partial structure in the referencechemical structure which corresponds to the expression region.

The neural network apparatus may receive a target property value as aninput. In an embodiment, the neural network apparatus may include a userinterface that is a means for inputting data for controlling the neuralnetwork apparatus. For example, the user interface may be a key pad, atouch pad, or the like, but is not limited thereto.

The target property value is a numerical value of a particular propertyof a chemical structure to be finally generated in the neural networkapparatus. In an embodiment, the target property value may be arefractive index value, an elastic modulus, a melting point, atransmission wavelength, and/or an emission wavelength. For example, theneural network apparatus may receive ‘emission wavelength: 350 nm’ as atarget property value. Alternatively, the target property value may beset in an increasing (+) direction or a decreasing (−) direction ratherthan a numerical value.

The neural network apparatus may generate a new chemical structurehaving a property value close to the target property value by changing apartial structure in the reference chemical structure.

In an embodiment, the neural network apparatus may output a newdescriptor by changing a bit value of an expression region of thereference descriptor. The partial structure in the reference chemicalstructure may be changed as the bit value of the expression region ofthe reference descriptor is changed. A method of changing the bit valueof the expression region may use a genetics algorithm, but is notlimited thereto.

The neural network apparatus may output a structure characteristic valuecorresponding to the new descriptor as output data of the neural networkby inputting the new descriptor, in which the bit value of theexpression region of the reference descriptor is changed, as input dataof the trained neural network (e.g., RNN) and driving the neuralnetwork. The neural network apparatus may generate a new chemicalstructure based on the output structure characteristic value.Alternatively, the neural network apparatus may use a factor for the newdescriptor, output in a learning process of the DNN, as input data ofthe trained neural network (e.g., RNN).

The neural network apparatus may iteratively generate a chemicalstructure through the above-described process until a chemical structurehaving a property value close to a target property value (e.g.,‘emission wavelength: 350 nm’) is generated.

Specifically, the neural network apparatus may compare a property valuefor a new chemical structure to a target property value and generate anew chemical structure again by changing the bit value of the expressionregion of the reference descriptor when the property value for the newchemical structure is less than the target property value.

When the property value of the new chemical structure generated throughthe above-described process is equal to or greater than the targetproperty value, the neural network apparatus may store the generated newchemical structure in a memory.

FIG. 12 is a flowchart of a method of generating a new chemicalstructure by changing an image for a chemical structure in a neuralnetwork apparatus according to an embodiment.

Hereinafter, descriptions that are the same as those given withreference to FIG. 11 are omitted.

Referring to FIG. 12, in operation 1210, the neural network apparatusmay obtain an image for a reference chemical structure.

In an embodiment, the image for the reference chemical structure mayinclude n×m pixels (where n and m are natural numbers). For example, 8bits, i.e., a value from 0 (black) to 255 (white), may be assigned toeach pixel of the image.

Hereinafter, the image for the reference chemical structure will bereferred to as a reference image.

In operation 1220, the neural network apparatus may input the referenceimage into a trained neural network and output a property value of aparticular property for the reference chemical structure.

A memory of the neural network apparatus may store an image for aparticular chemical structure and a property value numericallyrepresenting the property of the particular chemical structure, whichmatch each other as one set.

In an embodiment, the neural network apparatus may allow a neuralnetwork (e.g., DNN) to learn by using an image and a property value,stored in a memory. In a learning process using the image and theproperty value, a factor defining the relationship between the image andthe property value may be determined in the neural network.

The neural network apparatus may output a property value correspondingto the reference image as output data of the neural network by inputtingthe reference image as input data of the trained neural network anddriving the neural network.

In operation 1230, the neural network apparatus may determine anexpression region that expresses a particular property in the image.

A plurality of nodes of an input layer of the neural network mayrespectively correspond to pixel values constituting the image. Theneural network apparatus may obtain a node of the input layer, i.e.,pixel coordinates (or expression region) of the reference image, whichhas the greatest relevance in the expression of a particular propertyvalue of the reference chemical structure, through the interpretationprocess. Because the expression region of the reference imagecorresponds to a particular partial structure in the reference chemicalstructure, the neural network apparatus may determine a particularpartial structure, which has the greatest relevance in the expression ofa particular property value of the reference chemical structure, byobtaining the expression region of the reference image through theinterpretation process.

In operation 1240, the neural network apparatus may generate a newchemical structure by changing a partial structure in the referencechemical structure which corresponds to the expression region.

In an embodiment, the neural network apparatus may generate a new imageby changing a pixel value of the expression region of the referenceimage and/or a pixel value around the expression region. A partialstructure in the reference chemical structure may be changed as thepixel value of the expression region of the reference image and/or thepixel value around the expression region are changed. In an embodiment,the pixel value of the expression region of the reference image and/orthe pixel value around the expression region may be changed by usingGaussian Noise, but a method of changing the pixel value is not limitedthereto.

The neural network apparatus may output a structure characteristic valuecorresponding to the new image as output data of the neural network byinputting the new image, in which the pixel value of the expressionregion of the reference image and/or the pixel value around theexpression region are changed, as input data of the trained neuralnetwork (e.g., RNN) and driving the neural network. The neural networkapparatus may generate a new chemical structure based on the outputstructure characteristic value. Alternatively, the neural networkapparatus may use a factor for the new image, output in a learningprocess of the DNN, as input data of the trained neural network (e.g.,RNN).

The neural network apparatus may iteratively generate a chemicalstructure through the above-described process until a chemical structurehaving a property value close to a target property value (e.g.,‘emission wavelength: 350 nm’) is generated.

Specifically, the neural network apparatus may compare a property valuefor a new chemical structure to a target property value and generate anew chemical structure again by changing the pixel value of theexpression region of the reference image and/or the pixel value aroundthe expression region when the property value for the new chemicalstructure is less than the target property value.

When the property value of the new chemical structure generated throughthe above-described process is equal to or greater than the targetproperty value, the neural network apparatus may store the generated newchemical structure in a memory.

According to the aforementioned embodiments, a trained neural networkmay be interpreted to specify a partial structure expressing a propertyof a chemical structure. In addition, a new chemical structure having animproved property may be generated by changing the specified partialstructure.

Also, the aforementioned embodiments may be embodied in the form of arecording medium storing instructions executable by a computer, such asa program module, executed by a computer. The computer-readable mediummay be any recording medium that may be accessed by a computer and mayinclude volatile and non-volatile media and removable and non-removablemedia. Also, the computer-readable medium may include computer storagemedia and communication media. The computer storage media includevolatile and non-volatile and removable and non-removable mediaimplemented using any method or technology to store information such ascomputer-readable instructions, data structures, program modules, orother data. The communication media include computer-readableinstructions, data structures, program modules, or other data in amodulated data signal, or other transport mechanisms and include anydelivery media.

In addition, throughout the specification, the term “unit” may be ahardware component such as a processor or a circuit and/or a softwarecomponent executed by the hardware component such as a processor.

The above description of the disclosure is provided for the purpose ofillustration, and it would be understood by those skilled in the artthat various changes and modifications may be made without changingtechnical conception and essential features of the disclosure. Thus, itis clear that the above-described illustrative embodiments areillustrative in all aspects and do not limit the disclosure. Forexample, each component described to be of a single type may beimplemented in a distributed manner. Likewise, components described tobe distributed may be implemented in a combined manner.

It should be understood that embodiments described herein should beconsidered in a descriptive sense only and not for purposes oflimitation. Descriptions of features or aspects within each embodimentshould typically be considered as available for other similar featuresor aspects in other embodiments.

While one or more embodiments have been described with reference to thefigures, it will be understood by those of ordinary skill in the artthat various changes in form and details may be made therein withoutdeparting from the spirit and scope as defined by the following claims.

What is claimed is:
 1. A method of generating a chemical structure byusing a neural network apparatus, the method comprising: inputting adescriptor of a chemical structure to a trained neural network thatgenerates a property value of a property of the chemical structure, thedescriptor of the chemical structure representing structuralcharacteristics of the chemical structure and the property of thechemical structure being a characteristic possessed by the chemicalstructure; determining an expression region for expressing the propertyin the descriptor, the expression region comprising a bit position inthe descriptor; and generating a new chemical structure by modifying apartial structure in the chemical structure, the partial structurecorresponding to the expression region.
 2. The method of claim 1,wherein the determining comprises: determining the expression region forexpressing the property in the descriptor by the trained neural networkperforming an interpretation process to determine whether the propertyvalue is expressed by the partial structure in the chemical structure.3. The method of claim 2, wherein the determining comprises: determiningthe expression region for expressing the property in the descriptor byapplying a layer-wise relevance propagation (LRP) technique to thetrained neural network, wherein an activation function applied to a nodeof the trained neural network is designated as a linear function toapply the LRP technique to the trained neural network, and a mean squareerror (MSE) is designated for optimization.
 4. The method of claim 1,wherein the generating comprises: obtaining a bit value of the bitposition of the expression region in the descriptor; and generating thenew chemical structure by applying a genetics algorithm to the bit valueof the bit position and modifying the partial structure corresponding tothe expression region.
 5. The method of claim 1, wherein the generatingcomprises: generating a new first chemical structure by modifying thepartial structure in the chemical structure, the partial structurecorresponding to the expression region; inputting a descriptor for thenew first chemical structure to the trained neural network to output aproperty value of a particular property for the new first chemicalstructure; and generating a new second chemical structure by changing apartial structure in the new first chemical structure, the partialstructure corresponding to the expression region, when the propertyvalue of the particular property for the new first chemical structure isless than a preset value, and storing the new first chemical structurewhen the property value of the particular property for the new firstchemical structure is equal to or greater than the preset value.
 6. Aneural network apparatus configured to generate a chemical structure,the neural network apparatus comprising: a memory configured to store atleast one program; and a processor configured to control the neuralnetwork apparatus to implement a neural network by executing the atleast one program, which when the at least one program is executed theprocessor is configured to: input descriptor of a chemical structure toa trained neural network that generates a property value of a propertyof the chemical structure, the descriptor of the chemical structurerepresenting structural characteristics of the chemical structure andthe property of the chemical structure being a characteristic possessedby the chemical structure; determine an expression region for expressingthe property in the descriptor, the expression region comprising a bitposition in the descriptor; and generate a new chemical structure bymodifying a partial structure in the chemical structure, the partialstructure corresponding to the expression region.
 7. The neural networkapparatus of claim 6, wherein the processor when the at least oneprogram is executed is further configured to determine the expressionregion for expressing the property in the descriptor by the trainedneural network performing an interpretation process to determine whetherthe property value is expressed by the partial structure in the chemicalstructure.
 8. The neural network apparatus of claim 7, wherein theprocessor when the at least one program is executed is furtherconfigured to: determine the expression region for expressing theproperty in the descriptor by applying a layer-wise relevancepropagation (LRP) technique to the trained neural network; and designatean activation function applied to a node of the trained neural networkas a linear function to apply the LRP technique to the trained neuralnetwork and designate a mean square error (MSE) for optimization.
 9. Theneural network apparatus of claim 6, wherein the processor when the atleast one program is executed is further configured to obtain a bitvalue of the bit position of the expression region in the descriptor andto generate the new chemical structure by applying a genetics algorithmto the bit value of the bit position and modifying the partial structurecorresponding to the expression region.
 10. The neural network apparatusof claim 6, wherein the processor when the at least one program isexecuted is configured to: generate a new first chemical structure bymodifying the partial structure in the chemical structure, the partialstructure corresponding to the expression region; input a descriptor forthe new first chemical structure to the trained neural network to outputa property value of a particular property for the new first chemicalstructure; and generate a new second chemical structure by changing apartial structure in the new first chemical structure, the partialstructure corresponding to the expression region, when the propertyvalue of the particular property for the new first chemical structure isless than a preset value, and store the new first chemical structure inthe memory when the property value of the particular property for thenew first chemical structure is equal to or greater than the presetvalue.
 11. A method of generating a chemical structure by using a neuralnetwork apparatus, the method comprising: inputting an image of achemical structure to a trained neural network that generates a propertyvalue of a property of the chemical structure, the image of the chemicalstructure representing structural characteristics of the chemicalstructure and the property of the chemical structure being acharacteristic possessed by the chemical structure; determining anexpression region for expressing the property in the image, theexpression region comprising one or more pixels in the image; andgenerating a new chemical structure by modifying a partial structure inthe chemical structure, the partial structure corresponding to theexpression region.
 12. The method of claim 11, wherein the determiningcomprises: determining the expression region for expressing the propertyin the image by the trained neural network performing an interpretationprocess to determine whether the property value is expressed by thepartial structure in the chemical structure.
 13. The method of claim 12,wherein the determining comprises: determining the expression region forexpressing the property in the image by applying a layer-wise relevancepropagation (LRP) technique to the trained neural network, wherein anactivation function applied to a node of the trained neural network isdesignated as a linear function to apply the LRP technique to thetrained neural network, and a mean square error (MSE) is designated foroptimization.
 14. The method of claim 11, wherein the generatingcomprises: obtaining pixel values of the one or more pixels of theexpression region in the image; and generating the new chemicalstructure by applying Gaussian noise to the pixel values of the one ormore pixels and modifying the partial structure corresponding to theexpression region.
 15. The method of claim 11, wherein the expressionregion comprises a plurality of expression regions expressing theproperty and the generating comprises: obtaining coordinate informationin the image corresponding to the plurality of expression regions;calculating a center point in the image of the plurality of expressionregions based on the coordinate information and obtaining a pixel valueof the center point; and generating the new chemical structure byapplying Gaussian noise to the pixel value and modifying the partialstructure corresponding to the center point.
 16. The method of claim 11,wherein the generating comprises: generating a new first chemicalstructure by modifying the partial structure in the chemical structure,the partial structure corresponding to the expression region; inputtingan image for the new first chemical structure to the trained neuralnetwork to output a property value of a particular property for the newfirst chemical structure; and generating a new second chemical structureby changing a partial structure in the new first chemical structure, thepartial structure corresponding to the expression region, when theproperty value of the particular property for the new first chemicalstructure is less than a preset value, and storing the new firstchemical structure when the property value of the particular propertyfor the new first chemical structure is equal to or greater than thepreset value.
 17. A neural network apparatus configured to generate achemical structure, the neural network apparatus comprising: a memoryconfigured to store at least one program; and a processor configured tocontrol the neural network apparatus to implement a neural network byexecuting the at least one program, which when the at least one programis executed the processor is configured to: input an image of a chemicalstructure to a trained neural network that generates a property value ofa property of the reference chemical structure, the image of thechemical structure representing structural characteristics of thechemical structure and the property of the chemical structure being acharacteristic possessed by the chemical structure; determine anexpression region for expressing the property in the image, theexpression region comprising one or more pixels in the image; andgenerate a new chemical structure by modifying a partial structure inthe chemical structure, the partial structure corresponding to theexpression region.
 18. The neural network apparatus of claim 17, whereinthe processor when the at least one program is executed is furtherconfigured to determine the expression region for expressing theproperty in the image by the trained neural network performing aninterpretation process to determine whether the property value isexpressed by the partial structure in the chemical structure.
 19. Theneural network apparatus of claim 18, wherein the processor when the atleast one program is executed is further configured to: determine theexpression region for expressing the property in the image by applying alayer-wise relevance propagation (LRP) technique to the trained neuralnetwork; and designate an activation function applied to a node of thetrained neural network as a linear function to apply the LRP techniqueto the trained neural network and designate a mean square error (MSE)for optimization.
 20. The neural network apparatus of claim 17, whereinthe processor when the at least one program is executed is furtherconfigured to obtain pixel values of the one or more pixels of theexpression region in the image and to generate the new chemicalstructure by applying Gaussian noise to the pixel values of the one ormore pixels and modifying the partial structure corresponding to theexpression region.
 21. The neural network apparatus of claim 17, whereinthe expression region comprises a plurality of expression regionsexpressing the property and the processor when the at least one programis executed is further configured to: obtain coordinate information inthe image corresponding to the plurality of expression regions;calculate a center point in the image of the plurality of expressionregions based on the coordinate information and obtaining a pixel valueof the center point; and generate the new chemical structure by applyingGaussian noise to the pixel value and modifying the partial structurecorresponding to the center point.
 22. The neural network apparatus ofclaim 17, wherein the processor when the at least one program isexecuted is further configured to: generate a new first chemicalstructure by modifying the partial structure in the chemical structure,the partial structure corresponding to the expression region; input animage for the new first chemical structure to the trained neural networkto output a property value of a particular property for the new firstchemical structure; and generate a new second chemical structure bychanging a partial structure in the new first chemical structure, thepartial structure corresponding to the expression region, when theproperty value of the particular property for the new first chemicalstructure is less than a preset value, and store the new first chemicalstructure in the memory when the property value of the particularproperty for the new first chemical structure is equal to or greaterthan the preset value.
 23. A non-transitory computer-readable recordingmedium comprising a program, which, when executed by a computer,performs the method of claim
 1. 24. A non-transitory computer-readablerecording medium comprising a program, which, when executed by acomputer, performs the method of claim 11.